Electroencephalography microstate alterations reflect potential double‐edged cognitive adaptation in Ménière's disease

Abstract Purpose To explore the microstate characteristics and underlying brain network activity of Ménière's disease (MD) patients based on high‐density electroencephalography (EEG), elucidate the association between microstate dynamics and clinical manifestation, and explore the potential of EEG microstate features as future neurobiomarkers for MD. Methods Thirty‐two patients diagnosed with MD and 29 healthy controls (HC) matched for demographic characteristics were included in the study. Dysfunction and subjective symptom severity were assessed by neuropsychological questionnaires, pure tone audiometry, and vestibular function tests. Resting‐state EEG recordings were obtained using a 256‐channel EEG system, and the electric field topographies were clustered into four dominant microstate classes (A, B, C, and D). The dynamic parameters of each microstate were analyzed and utilized as input for a support vector machine (SVM) classifier to identify significant microstate signatures associated with MD. The clinical significance was further explored through Spearman correlation analysis. Results MD patients exhibited an increased presence of microstate class C and a decreased frequency of transitions between microstate class A and B, as well as between class A and D. The transitions from microstate class A to C were also elevated. Further analysis revealed a positive correlation between equilibrium scores and the transitions from microstate class A to C under somatosensory challenging conditions. Conversely, transitions between class A and B were negatively correlated with vertigo symptoms. No significant correlations were detected between these characteristics and auditory test results or emotional scores. Utilizing the microstate features identified via sequential backward selection, the linear SVM classifier achieved a sensitivity of 86.21% and a specificity of 90.61% in distinguishing MD patients from HC. Conclusions We identified several EEG microstate characteristics in MD patients that facilitate postural control yet exacerbate subjective symptoms, and effectively discriminate MD from HC. The microstate features may offer a new approach for optimizing cognitive compensation strategies and exploring potential neurobiological markers in MD.


| INTRODUC TI ON
Meniere's Disease (MD) is a multifactorial inner ear disorder with unpredictable episodes and variable severity, characterized by recurrent spontaneous vertigo, fluctuating sensorineural hearing loss, tinnitus, and aural fullness. 1,2[4] As a lead to the loss of quality-associated life-years, patients with MD commonly experience gait and balance impairment and, in severe cases, may suffer from "drop attack," where sudden distortion of vertical orientation leads to a fall without loss of consciousness. 2 Various vestibular testing has been considered to have a supportive role, 1,5,6 but the heterogeneity of the results and the partial inconsistency with subjective symptoms suggest that new assessment methods and further insights into the pathophysiology of balance impairment from additional perspectives may be needed.
][10][11][12] Previous studies reported a decreased volume of hippocampus in patients with MD, which correlated with the severity of hearing and vestibular loss. 13,14In addition to the structural investigations indicating an impact of MD in brain areas, the development of functional neuroimaging tools has provided more opportunities to understand the dynamic neural function in MD.
Despite the wide application of various neuroimaging approaches in neurological and psychological areas, there were relatively few studies focused on the intrinsic functional networks and neural activity in MD patients.Recently, a resting-state functional magnetic resonance imaging (rs-fMRI) study reported the functional reorganization of intranetwork and internetwork connectivity in MD patients, manifesting as a decreased functional connectivity in the somatomotor, auditory, ventral attention, default mode, limbic, and deep gray matter networks. 10The correlations between some altered brain network indexes and the clinical stages suggested that largescale network reorganizations revealed by neuroimaging techniques could provide further understanding of MD's pathology.However, the indicators correlated with the severity of clinical complaints and balance performance have yet to be investigated.
Comparing to other neuroimaging tools, the high temporal resolution, comparatively lower cost, adaptability to different conditions, and convenience in acquisition of electroencephalography (EEG) provide it more chance for clinical applications.Microstates are non-overlapping and quasi-stable topography patterns of EEG scalp potential fields which reveals dynamical activity of large-scale brain networks on a subsecond timescale. 15,16Comparing to rs-fMRI which focuses on the strength of relationships among cortical networks, EEG microstates provide more information about their patterns of temporal activity. 16,17Recently, the EEG microstate analysis has achieved fascinating results in numerous neurological and psychological diseases, [18][19][20][21][22] showing huge potential as biomarkers or phenotype candidates for various neurological conditions.
Currently, to the best of our knowledge, the microstate profiles of patients with MD remain unknown.The aim of our study was to identify the altered microstate pattern of patients with MD, explore its clinical significance, and test whether these microstate characteristics could potentially be the biomarker for MD.In order to get more systemic diseases, psychiatric disorders or neurological disorders (i.e., encephalitis, epilepsy).All participants were right-handed and provided written informed consent after a detailed explanation.This study was approved by the Institutional Ethics Review Board of the Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Approval No: 2020-128).A schematic overview of the research is shown in Figure 1.

| Clinical assessment
We used the vertigo symptom scale (VSS) to evaluate the severity of vestibular symptoms, including the frequency of dizziness, vertigo, imbalance, and related autonomic symptoms during the past 12 months.It contains two subscales, the anxiety and autonomic symptom scale (VSS-AA) and the vertigo symptom scale (VSS-VER). 23In addition, the 9-item patient health questionnaire subscale (PHQ -9) 24,25 and the 7-item generalized anxiety disorder scale (GAD-7) 26 were employed to assess depression and anxiety, respectively. 27re-tone audiometry was obtained at 0.25-8 kHz (GSI-61; Grason-Stadler, Inc.) and the pure-tone average (PTA) was calculated as the simple arithmetic means of 0.5, 1, and 2 kHz pure-tone threshold.Staging was determined based on the worst recorded PTA during intermittent periods over the past 6 months: Stage I is defined as a PTA of ≤25 dBHL, Stage II as 26-40 dBHL, Stage III as 41-70 dBHL, and Stage IV as >70 dBHL. 28e computerized dynamic posturography (CDP) was used to evaluate the balance performance and postural stability (Equitest, NeuroCom International, Inc., Clackamas, OR, United States).The equilibrium composite score (EC) of the whole sensory organization test (SOT) was derived from six individual equilibrium scores (ES), obtained in six progressively challenging conditions subjectively. 29es were instructed to be closed in condition 2 and 5, while the support platform was sway-referenced in condition 3 to 6.The visual surround was also sway-referenced in condition 3 and 6.The abil- F I G U R E 1 Overview of the main steps of analysis.Following the preprocessing of hd-EEG data, we calculated the GFP for each participant.The topologies at local maxima of the GFP were subjected to the AAHC algorithm, resulting in four microstate templates.After backfitting the group maps to the original data, we computed the microstate statistics based on the microstate sequences.We then performed a cross-sectional comparison of the microstate characteristics between MD patients and HC participants and further analyzed the correlations between microstate characteristics and clinical measures in MD patients.The significant characteristics identified through crosssection analysis were subsequently fed into the SVM classifier to evaluate the potential of microstate characteristics as future neurobiomarkers.

| Electroencephalography data acquisition and preprocessing
A 256-channel system (Electrical Geodesics, Eugene, OR, United

| Microstate analysis
Consistent with previous work, the microstate analysis was conducted using the Microstate Analysis plugin (Version 0.3, Thomas König, University of Bern, Switzerland) for EEGLAB.After a bandpass filter between 2 and 20 Hz, the global field power (GFP) was calculated.The local maxima of the GFP curve reflect the moments of highest field intensity and highest topographical signal to noise ratio.
Thus we submitted only EEG topographies at the GFP peaks to the "atomize and agglomerate hierarchical clustering" (AAHC) algorithm and figure out the four most dominant template maps ignoring polarity inversion. 15,32The group templates were further calculated separately through a second AAHC cluster analysis across the individual template maps of each subject.Base on the spatial correlation, we back fit the group template into each individual data and calculated the global explained variance to evaluate how well our templates explained the EEG topography throughout the time series.
At last, we calculated the classical dynamic characteristics for each microstate class: mean duration, time coverage, frequency of occurrence, and transitions between each couple of microstate classes.Mean duration (ms) is the average duration of time in which a given microstate remains constant.Time coverage (%) is the percentage of the total analysis time spent in a given microstate, also known as "Contribution."Occurrence is the average number of times per second that a specific microstate becomes dominant. 15,16,33

| Machine learning
To investigate the potential of microstate features to differentiate MD patients from HC, we established a support vector machine (SVM) classifier with a linear kernel based on the microstate and demographic features.The feature selection was followed a backward strategy.Considering our limited sample size, we obtained the machine parameters and performance estimation via a nested leave-one-out cross-validation framework.The penalty parameter C was optimized by a fivefold grid search process.We computed the accuracy, sensitivity, specificity, and balanced accuracy to measure its performance.The balanced accuracy was calculated as the average of sensitivity and specificity.To avoid potential problems arising from the lack of independence, we further conducted a permutation test with 5000 iterations to verify the significance of our model.Machine learning was performed with Scikit-learn in Python (version 3.8.8).

| Statistical analysis
Statistical tests were performed with IBM SPSS 26 (IBM Corp., Chicago, USA) and GraphPad Prism Software (San Diego, CA).
Shapiro-Wilk's normality tests were performed for continuous variables.Continuous variables were reported as mean ± standard deviation or median (interquartile range) according to their distribution.
Unpaired t-test and Mann-Whitney test were used to compare continuous variables, depending on their distribution.Nonparametric chi-squared test was employed to compare gender ratio.To assess between-group differences in microstate parameters, an analysis of two-way analysis of variance (ANOVA) with post hoc group comparisons was applied.The Group and Microstate Class were taken as factors.Bonferroni's correction was used for correcting multiple comparisons.A p level of less than 0.05 (two-sided) was considered to be statistically significant.Correlation between two variables was determined by Spearman correlation.

| Demographics and basic characteristics
As presented in Table 1, there was no statistically significant difference between MD patients and HC in terms of demographic characteristics such as age (t = 0.5723, p = 0.5693), gender (x 2 = 0.1250, p = 0.7237), and education year (U = 440, p = 0.7239).As expected, MD patients demonstrated a significant increase in PTA thresholds (t = 3.247, p = 0.0019).However, despite significant complaints of vertigo symptoms (U = 41, p<0.0001), no significant differences were observed in the ECs of computerized dynamic posturography (t = 0.5750, p = 0.5675).Consistent with previous studies, MD patients also obtained significantly higher scores on PHQ9, GAD7, and PSQI scales.

| Microstate topographies and dynamic parameters
As shown in Figure 2a

| Correlation between microstate and clinical characteristics
Our correlation analysis revealed no statistically significant association between microstate dynamics and PTA, duration of history, PHQ-9 nor GAD7.However, VSS scores were negatively correlated with the transition between class A and B (p = 0.0138, R = −0.431;p = 0.0379, R = −0.369)(Figure 3a).To prevent false positives caused by the impact of autonomic symptoms, we further confirmed the correlations between microstate characteristics and vestibular subscale of VSS (p = 0.0057, R = −0.478;p = 0.0164, R = −0.421)(Figure 3a,b).

| DISCUSS ION
The present study investigated the brain dynamics in MD patients with an EEG microstate analysis, which demonstrated alterations in the pattern of neural network transitions on a sub-second timescale.In our study, microstate class C exhibited higher time coverage and occurrence in MD patients compared to HC participants.
According to previous studies, microstate class C is associated with the saliency network (SN), reflecting activation in the dorsal anterior cingulate cortex, inferior frontal cortices, and anterior insula. 15,34,35r results regarding class C indicate more frequent but stable activations of the SN in MD patients during the resting state.As we know, the SN plays an important role in identifying the most homeostatically relevant stimuli among multiple competing external and internal stimuli, 36 acting as a gatekeeper of executive control and showing increased activation in stimulus-driven cognitive processing. 37Executive function, especially inhibitory control, has been reported to play a crucial role in dynamic balance control and postural adjustment. 38  Similarly, the platform and eyes state remain the same in condition 6, while the visual surround becomes sway-referenced, providing not only somatosensory but also visual information. 30tably, the balance performance and sensory strategy exhibited no such significant correlations in condition 5 that deprived visual input by closing eyes based on condition 4 (Figure 3d).shown to originate from the bilateral superior and middle temporal lobe, associated with specific auditory and visual processing, particularly in spatial visualization tasks. 15,33,34The vestibular cortex within temporoparietal association cortex is involved in the visual-vestibular sensory conflict and plays an important role in the construction of internal model of self-body, providing fundamental information such as body schema and verticality. 7,39The increased transitions from microstate class A to C may imply the increased information interaction between these networks to adjust body scheme and facilitate cognitive processing of anticipatory postural adjustment.
As for the syntax of microstate class A in MD patients, the transitions between class A and B also decreased significantly and showed a negative correlation with the vestibular symptom scales, indicating a positive correlation with vertigo severity (Figure 2e,f, Figure 3a,b).The microstate class B is reported to be associated with visual network and involved in visual-spatial attention and processing. 15,33,40Both of the classes are engaged in externally oriented sensory-cognitive processes, and the binary microstate loop between them has been reported to be dominant in awake and resting-state 41 The disruption of direct transition within the binary loop in MD patients may partly due to the hearing loss.But with the lack of correlation with PTA threshold, it could be better explained by the engagement of other cognitive processing during the spontaneously repetitive scanning of sensory input, including the inhibitory control of SN mentioned above.Although the consciously processing could facilitate postural control as we discussed above, it may exacerbate the subjective symptoms at the same time.This would be consistent with the recent study that revealed the role of conscious movement processing in the formation of distorted perceptions of unsteadiness. 42The microstate profiles of MD patients may potentially serve as markers for measuring such conscious processing, providing insights into future investigations of potential therapies aimed at optimizing cognitive compensation strategies.
Consistent with our study, the recent rs-fMRI study in MD patients 10 reported a similar positive correlation between residual vestibular function and the intranetwork connectivity within the ventral attention network despite different methods for neural network delineation, potentially acting as compensatory mechanisms.
Inconsistently, our results did not detect any alterations significantly correlated to PTA threshold or clinical stages determined by hearing loss.The lack of correlation is similar to the microstate findings in patients with idiopathic sudden sensorineural hearing loss, 43 despite hearing loss being widely believed to cause extensive functional network alterations. 44,45This may be attributed to the methodological characteristics of microstate studies, which emphasize the sequential activation of networks and are less sensitive to the functional connectivity between networks.Different microstate patterns have been raised as potential biomarkers or candidate endophenotype in various neurological and psychological diseases, 16 including schizophrenia, 19,46 dementia, 18,47 and disorders of consciousness. 21The affordability and convenience of EEG provide it more chance for clinical applications compared to other neuroimaging modalities.In order to preliminarily explore the potential of microstate profiles as

| 3 of 10 LI
reproducible and explainable results, the four canonical classes of microstates were taken in the present study.A detailed correlation analysis with balance performance and vertigo severity was conducted to further interpret the underlying neural mechanism of postural control.Finally, a machine learning classifier was established to evaluate how well the microstate profiles may serve as a biomarker for MD by distinguishing patients from healthy participants.The results showed that the temporal dynamics of microstates class C and the syntax of class A could be candidate neural signatures for MD.The findings shall provide insights into the neurobiological mechanisms of balance impairment in MD and a potential feature to facilitate clinical diagnosis.2 | ME THODS 2.1 | Participants A total of 32 patients diagnosed with MD were recruited from the Otolaryngology-Head and Neck Surgery Department of the Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine.The clinical diagnosis of MD is established by experienced professional doctors according to the criteria outlined by the Bárány Society.We also recruit 29 age-and gender-matched healthy controls (HC) with no history of vertigo attacks.The exclusion criteria for both groups included: (1) age below 18 years old; (2) inability to complete the questionnaire and vestibular function tests with the help of doctors and researchers, including MD patients during acute attacks; (4) a current or longterm history of substance dependence; (5) comorbid with severe optimizing cognitive compensation strategies and exploring potential neurobiological markers in MD.K E Y W O R D S cognitive adaptation, EEG, Ménière's disease, microstate, postural control et al.
States) was used to acquire high-density EEGs (hd-EEG) with a sampling rate of 1000 Hz.Participants were instructed to sit comfortably in a sound-shield room, to remain relaxed and awake, and to minimize eye-movement throughout the recording.Restingstate EEGs were recorded for 7 min in both eyes-closed and eyesopen paradigm.Only eyes-closed EEG data were analyzed.Custom MATLAB (Mathworks, Massachusetts, USA) scripts based on EEGlab toolbox 31 (Delorme and Makeig, 2004) were used for offline preprocessing.The data were filtered with a bandpass of 0.5-200 Hz, downsampled to 500 Hz, topographically interpolated bad channels, segmented into 2-s segments, and re-referenced to the grand average.The eye movement, heartbeat, and muscle artifacts were removed by independent component analysis (ICA).
, we identified four dominant microstate classes labeled as A, B, C, and D, resembling classic templates commonly reported in prior studies.These classes, respectively, explained 78.38% and 77.07% of the global variance in MD patients and HC participants.The Group average statistics of mean duration, time coverage, and frequency of occurrence are depicted in Figure 2b-d.Two-way repeated measures (rm) ANOVAs showed significant Microstate Class × Group interaction for time coverage (F = 6.023, p = 0.0006) and occurrence (F = 10.70,p < 0.0001), indicating a dependence of group difference on microstate class.No significant interaction was detected for mean duration (F = 2.432, p = 0.0667).Post hoc tests (Table 2) showed that MD patients had increased occurrence (p = 0.0001) and time coverage (p = 0.0049) of class C. As for the microstate syntax, the transitions are depicted in Table 3.Compared to the HC group, MD patients showed fewer transitions between class A and B (both, p < 0.001) and between class A and D (both, p < 0.05).In addition, transitions from class A to C (p = 0.0459) and from class B to C (p = 0.0365) both increased in MD patients (Figure 2e,f).

F I G U R E 2 6 (
Note: *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.The spatial configuration and dynamic characteristics of microstate classes.(a) Global architecture of the four microstate classes, separately for MD patients and HC participants.(b-d) Group average temporal parameters, (b) mean duration, (c) time coverage, and (d) occurrence frequency, of each class for MD patients versus HC participants.(e, f) Microstate syntax analysis results.(e) Group average transitions and group comparisons.(f) Schematic view of microstate syntax.Data of MD patients are displayed in red dots and HC in green triangles.Significant increases in MD are represented by solid red arrows, significant decreases by solid blue arrows, and no difference by dashed arrows.Error bars represent SEM.*p < 0.05, **p < 0.01, ***p < 0.001 by post hoc Bonferroni's multiple comparisons tests.Additionally, the transition from class A to C was positively correlated with the equilibrium score in condition 4 (p = 0.0277, R = 0.389) and condition 6 (p = 0.0405, R = 0.364).Thus it was also positively correlated with the overall score for visual system (p = 0.0493, R = 0.350).Considering the associations between microstate dynamics and balance performance in challenging conditions revealed by CDP, we further performed another correlation analysis to explore the relationship between microstate dynamics and strategy scores.The occurrence of class C as well as transitions from A to C turned out to be positively correlated with SC in both condition 4 and condition Figure 3d,e), while the time coverage of class C and transitions from B to C were only positively correlated with SC in condition 4 (Figure 3d,f).

3. 4 |
Classification Performance in SVM classifier based on microstate characteristics With feature reduction and model optimized by a backward strategy, the linear SVM classifier based on demographic characteristics and seven microstate features could differentiate MD patients from HC participants with an accuracy of 88.52%, a sensitivity of 86.21%, and a specificity of 90.61%.The confusion matrix and the weights of the five most important features are presented in Figure 4.The statistical significance of this model was further confirmed via permutation testing (p = 0.0002).
The distinct features in MD patients, primarily characterized by increased frequency of class C and syntax alterations of class A, showed significant correlations with both balance performance and vertigo symptoms.It also provided discriminative features to distinguish MD patients from HC participants, indicating a potential role of microstate signatures for clinical diagnosis and assessment.

F I G U R E 3
Correlations between microstates and clinical characteristics in MD patients.(a) Correlation matrix between differential microstate and clinical characteristics.(b) Scatterplot of transitions from class A to B and vestibular subscales of VSS.(c) Scatterplot of transition from class A to C and ES in condition 4. (d) Correlation matrix between differential microstate characteristics and both equilibrium and strategy scores.(e, f) Scatterplot of SC in condition 4 and transitions from class A to B (e), occurrence frequency of class C (f). Spearman R values are marked.*p < 0.05.PTA, pure-tone average; ECs, equilibrium composite scores; ES1-6, equilibrium score in condition 1-6; SOM, somatosensory ratios; VIS, visual ratios; VEST, vestibular ratios; PREF, visual preference; VSS, vertigo symptom scale; VSS-VER, vertigo symptom scale in VSS; VSS-AA, autonomic symptom scale in VSS; SC1-6, sensory strategy scores in condition 1-6.patients, the increases in microstate class C probably represent a compensation strategy to handle the competing vestibular information with greater involvement of cognitive processing.In the correlation analysis between microstate characteristics and ESs in challenging conditions from condition 3 to 6, the increase in occurrence frequency of class C showed a positive correlation with ES in condition 4 (Figure 3a,d).The further correlation analysis with strategy scores showed a similar positive correlation in both condition 4 and condition 6 (Figure 3d, f).In condition 4, the platform was sway-referenced with visual surround stable and the eyes open, providing conflicting somatosensory information and accurate visual and vestibular information.
The results indicated that the underlying neural assembly of microstate C modulated the vestibular-visual interaction to handle the balance challenge raised by somatosensory conflict, regardless of the reliability of the visual clues.However, when it comes to conflicting visual clues in condition 6, the strategy adjustment correlated with the increased occurrence frequency of microstate class C seemed insufficient to significantly improve balance performance.In this condition, the positive correlation of ES6 with the transitions from microstate class A to C (Figure 3c-e) indicated a more significant role of the microstate syntax, which reflecting an encoded sequential activation of neural networks.According to previous studies, microstate class A has been

F I G U R E 4 | 9 of 10 LI
Performance of SVM classifier based on microstate and demographic parameters.(a) Confusion matrix of SVM classification.(b) Feature weights of the five most important features in the classifier.(c) Post hoc test for SVM model via permutation testing with 5000 iterations.et al.

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clinical biomarkers for MD patients, we established a SVM classifier, trying to differentiate MD patients from HC and achieving an accuracy of 88.52% with a sensitivity of 86.21%, and a specificity of 90.61% at last.Considering the limited sample size included in this study, the scalability of this result needs further validation in larger datasets in the future.However, it also suggested that microstate features have the potential to serve as biomarkers in the future.Although numerous microstate analysis reported in literature has provided great support for interpreting the clinical and neuropsychological meaning of microstates, we still should be cautious to interpret the microstate results, especially considering the lack of a full understanding of the functional and pathophysiological details in MD patients.In addition, the affected lateral was not considered in the inclusion criteria, thus the possibility of lateralization issues should be taken into account in further studies.More studies of EEG changes and larger datasets in different MD types might reveal more valuable insights.Longitudinal studies with standard treatment may further elucidate its clinical potentials.CON CLUS ION In conclusion, the study first reported the patterns of EEG microstates in patients with MD.The syntax of microstate A and C demonstrated an extra significant conscious movement processing in MD, which may facilitate postural control but aggravate severity of subjective vertigo symptoms.The results provide a further understanding of balance problems in MD and shed a light on potential neurophysiological biomarkers for future MD diagnosis.AUTH O R CO NTR I B UTI O N S Y.L. and W.L. conceived and designed the study.Y.L., J.L. and W.L. collected the EEG data.P.W., D.Y., Z.C., Z.S., and W.Q. collected the clinical data.Y.L., J.L., and W.L. analyzed the data and wrote the paper together.W.L., W.Q., Y.W., D.Y., Z.C., and H.S. contributed to the manuscript revisions.H.S. administrated the project.
General demographic and clinical characteristics.
With fluctuating vestibular dysfunction in MD TA B L E 2 TA B L E 3